Mobile object control method, mobile object control device, and storage medium

ABSTRACT

A mobile object control method including: recognizing physical objects near a mobile object and a route shape; generating a target trajectory based on a result of the recognition; causing the mobile object to travel autonomously along the target trajectory; and determining that an abnormality has occurred in a control system for causing the mobile object to travel autonomously by performing the recognition when a degree of deviation between a reference target trajectory determined by the route shape and serving as a reference for generating the target trajectory and the target trajectory is greater than or equal to a first reference degree and output a determination result.

CROSS-REFERENCE TO RELATED APPLICATION

Priority is claimed on Japanese Patent Application No. 2020-061787,filed Mar. 31, 2020, the content of which is incorporated herein byreference.

BACKGROUND Field of the Invention

The present invention relates to a mobile object control method, amobile object control device, and a storage medium.

Description of Related Art

Research and practical application for causing a vehicle to travelautonomously (hereinafter referred to as “automated driving”) are inprogress (Japanese Unexamined Patent Application, First Publication No.2020-42853).

SUMMARY

In automated driving, the demand for reliability of a control system issignificantly high. Thus, even if there is no apparent failure, it isdesirable to determine some type of abnormality and performmaintenance/repair promptly. The same is true for movement control of avehicle and a mobile object that moves autonomously.

The present invention has been made in consideration of suchcircumstances, and an objective of the present invention is to provide amobile object control method, a mobile object control device, and astorage medium capable of determining an abnormality in a control systempromptly.

A mobile object control method, a mobile object control device, and astorage medium according to the present invention adopt the followingconfigurations.

(1): According to an aspect of the present invention, there is provideda mobile object control method including: recognizing physical objectsnear a mobile object and a route shape; generating a target trajectorybased on a result of the recognition; causing the mobile object totravel autonomously along the target trajectory; and determining that anabnormality has occurred in a control system for causing the mobileobject to travel autonomously by performing the recognition when adegree of deviation between a reference target trajectory determined bythe route shape and serving as a reference for generating the targettrajectory and the target trajectory is greater than or equal to a firstreference degree and output a determination result.

(2): In the above-described aspect (1), further including: performing aprocess of assigning a larger weight when a degree of change obtained bycomparing a deviation between coordinate points corresponding to thesame point in a traveling direction of the mobile object on thereference target trajectory and the target trajectory with a deviationbetween individual data elements corresponding to adjacent points in atleast the traveling direction of the mobile object is higher incorrespondence with the same point in a traveling direction of aplurality of mobile objects; and calculating a degree of deviationbetween the reference target trajectory and the target trajectory byaggregating deviations between the individual data elements that havebeen weighted.

(3): According to another aspect of the present invention, there isprovided a mobile object control method including: recognizing physicalobjects near a mobile object and a route shape; iteratively generating atarget trajectory based on a result of the recognition; causing themobile object to travel autonomously along the iteratively generatedtarget trajectory; and determining that an abnormality has occurred in acontrol system for causing the mobile object to travel autonomously byperforming the recognition when a degree of deviation between a firsttarget trajectory generated at a first time point and a second targettrajectory generated at a second time point different from the firsttime point is greater than or equal to a third reference degree andoutput a determination result.

(4): In the above-described aspect (3), further including: performing aprocess of assigning a larger weight when a degree of change obtained bycomparing a deviation between coordinate points corresponding to thesame point in a traveling direction of the mobile object on the firsttarget trajectory and the second target trajectory with a deviationbetween coordinate points corresponding to adjacent points in at leastthe traveling direction of the mobile object is higher in correspondencewith the same point in a traveling direction of a plurality of mobileobjects; and, calculating a degree of deviation between the first targettrajectory and the second target trajectory by aggregating deviationsbetween the coordinate points that have been weighted.

(5): In the above-described aspect (1), wherein the generating includessetting a risk that is an index value representing a degree at which themobile object should not approach based on the presence of therecognized physical object in an assumed plane represented in atwo-dimensional plane when a space near the mobile object is viewed fromabove and generates the target trajectory so that the mobile objectpasses through a point where the risk is low; and wherein the methodfurther comprises stopping the determining when a degree of risk basedon a risk value due to the presence of the physical object at each pointof the target trajectory is higher than or equal to a second referencedegree.

(6): In the above-described aspect (1), further including: acquiringsurrounding environment information of the mobile object; and making aprobability of the determining lower when the environment informationsatisfies a predetermined condition.

(7): In the above-described aspect (1), further including: acquiring aspeed of the mobile object; and making a probability of the determininglower when the speed is higher than a reference speed.

(8): In the above-described aspect (1), further including: collectingdata for each speed range of the mobile object; and wherein thedetermining is performed for each speed range of the mobile object.

(9): According to another aspect of the present invention, there isprovided a mobile object control device including: a storage devicestoring a program; and a hardware processor, wherein the hardwareprocessor executes the program stored in the storage device to:recognize physical objects near a mobile object and a route shape;generate a target trajectory based on a result of the recognition; causethe mobile object to travel autonomously along the target trajectory;and determine that an abnormality has occurred in a control system forcausing the mobile object to travel autonomously by performing therecognition when a degree of deviation between a reference targettrajectory determined by the route shape and serving as a reference forgenerating the target trajectory and the target trajectory is greaterthan or equal to a first reference degree and output a determinationresult.

(10): According to still another aspect of the present invention, thereis provided a mobile object control device including: a storage devicestoring a program; and a hardware processor, wherein the hardwareprocessor executes the program stored in the storage device to recognizephysical objects near a mobile object and a route shape; iterativelygenerate a target trajectory based on a result of the recognition; causethe mobile object to travel autonomously along the iteratively generatedtarget trajectory; and determine that an abnormality has occurred in acontrol system for causing the mobile object to travel autonomously byperforming the recognition when a degree of deviation between a firsttarget trajectory generated at a first time point and a second targettrajectory generated at a second time point different from the firsttime point is greater than or equal to a third reference degree andoutput a determination result.

(11): According to still another aspect of the present invention, thereis provided a computer-readable non-transitory storage medium storing aprogram to be executed by a computer to: recognize physical objects neara mobile object and a route shape; generate a target trajectory based ona result of the recognition; cause the mobile object to travelautonomously along the target trajectory; and determine that anabnormality has occurred in a control system for causing the mobileobject to travel autonomously by performing the recognition when adegree of deviation between a reference target trajectory determined bythe recognized route shape and serving as a reference for generating thetarget trajectory and the target trajectory is greater than or equal toa first reference degree and output a determination result.

(12): According to still another aspect of the present invention, thereis provided a computer-readable non-transitory storage medium storing aprogram to be executed by a computer to: recognize physical objects neara mobile object and a route shape; iteratively generate a targettrajectory based on a result of the recognition; cause the mobile objectto travel autonomously along the iteratively generated targettrajectory; and determine that an abnormality has occurred in a controlsystem for causing the mobile object to travel autonomously byperforming the recognition when a degree of deviation between a firsttarget trajectory generated at a first time point and a second targettrajectory generated at a second time point different from the firsttime point is greater than or equal to a third reference degree andoutput a determination result.

According to the above-described aspects (1) to (12), it is possible todetermine an abnormality in a control system promptly.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a configuration diagram of a vehicle system using a mobileobject control device according to an embodiment.

FIG. 2 is a functional configuration diagram of an automated drivingcontrol device.

FIG. 3 is a diagram showing an outline of risks set by a riskdistribution predictor.

FIG. 4 is a diagram showing values of a first risk and a second risktaken along line 4-4 of FIG. 3.

FIG. 5 is a first diagram for describing a process of a targettrajectory generator.

FIG. 6 is a second diagram for describing a process of the targettrajectory generator.

FIG. 7 is a diagram for describing content of a process of obtaining adegree of deviation between a reference target trajectory and a targettrajectory.

FIG. 8 is a diagram for describing content of a process of obtaining adegree of deviation between a first target trajectory and a secondtarget trajectory.

FIG. 9 is a diagram showing an example of a hardware configuration ofthe automated driving control device of the embodiment.

DESCRIPTION OF EMBODIMENTS

Hereinafter, embodiments of a mobile object control method, a mobileobject control device, and a storage medium of the present inventionwill be described with reference to the drawings. A mobile object is astructure, which is capable of being moved autonomously by a drivemechanism provided in the mobile object, such as a vehicle, anautonomous walking robot, or a drone. In the following description, itis assumed that the mobile object is a vehicle that moves on the groundand a configuration and a function for causing the vehicle to move onthe ground will be described exclusively. However, when the mobileobject is a flying object such as a drone, a configuration and afunction for moving in a three-dimensional space may be provided in theflying object.

First Embodiment [Overall Configuration]

FIG. 1 is a configuration diagram of a vehicle system 1 using a mobileobject control device according to an embodiment. For example, a vehiclein which the vehicle system 1 is mounted is a two-wheeled vehicle, athree-wheeled vehicle, or a four-wheeled vehicle. A driving source ofthe vehicle is an internal combustion engine such as a diesel engine ora gasoline engine, an electric motor, or a combination thereof. Theelectric motor is operated using electric power generated by an electricpower generator connected to the internal combustion engine or electricpower with which a secondary cell or a fuel cell is discharged.

For example, the vehicle system 1 includes a camera 10, a radar device12, a light detection and ranging (LIDAR) sensor 14, a physical objectrecognition device 16, a communication device 20, a human machineinterface (HMI) 30, a vehicle sensor 40, a navigation device 50, a mappositioning unit (MPU) 60, driving operators 80, an automated drivingcontrol device 100, a travel driving force output device 200, a brakedevice 210, and a steering device 220. Such devices and equipment areconnected to each other by a multiplex communication line such as acontroller area network (CAN) communication line, a serial communicationline, or a wireless communication network. The configuration shown inFIG. 1 is merely an example and parts of the configuration may beomitted or other configurations may be further added.

For example, the camera 10 is a digital camera using a solid-stateimaging element such as a charge coupled device (CCD) or a complementarymetal oxide semiconductor (CMOS). The camera 10 is attached to anylocation on the vehicle (hereinafter referred to as a host vehicle M) inwhich the vehicle system 1 is mounted. When the view in front of thehost vehicle M is imaged, the camera 10 is attached to an upper part ofa front windshield, a rear surface of a rearview mirror, or the like.For example, the camera 10 periodically and iteratively images thesurroundings of the host vehicle M. The camera 10 may be a stereocamera.

The radar device 12 radiates radio waves such as millimeter waves aroundthe host vehicle M and detects at least a position (a distance to and adirection) of a physical object by detecting radio waves (reflectedwaves) reflected by the physical object. The radar device 12 is attachedto any location on the host vehicle M. The radar device 12 may detect aposition and speed of the physical object in a frequency modulatedcontinuous wave (FM-CW) scheme.

The LIDAR sensor 14 radiates light (or electromagnetic waves having awavelength close to light) to the vicinity of the host vehicle M andmeasures scattered light. The LIDAR sensor 14 detects a distance to anobject based on time from light emission to light reception. Theradiated light is, for example, pulsed laser light. The LIDAR sensor 14is attached to any location on the host vehicle M.

The physical object recognition device 16 performs a sensor fusionprocess on detection results from some or all of the camera 10, theradar device 12, and the LIDAR sensor 14 to recognize a position, atype, a speed, and the like of a physical object. The physical objectrecognition device 16 outputs recognition results to the automateddriving control device 100. The physical object recognition device 16may output detection results of the camera 10, the radar device 12, andthe LIDAR sensor 14 to the automated driving control device 100 as theyare. The physical object recognition device 16 may be omitted from thevehicle system 1.

The communication device 20 communicates with another vehicle present inthe vicinity of the host vehicle M, or communicates with various typesof server devices via a radio base station, using, for example, acellular network or a Wi-Fi network, Bluetooth (registered trademark),dedicated short range communication (DSRC), or the like.

The HMI 30 presents various types of information to an occupant of thehost vehicle M and receives an input operation by the occupant. The HMI30 includes various types of display devices, a speaker, a buzzer, atouch panel, a switch, keys, and the like.

The vehicle sensor 40 includes a vehicle speed sensor configured todetect the speed of the host vehicle M, an acceleration sensorconfigured to detect acceleration, a yaw rate sensor configured todetect angular velocity around a vertical axis, a direction sensorconfigured to detect a direction of the host vehicle M, and the like.

For example, the navigation device 50 includes a global navigationsatellite system (GNSS) receiver 51, a navigation HMI 52, and a routedeterminer 53. The navigation device 50 stores first map information 54in a storage device such as a hard disk drive (HDD) or a flash memory.The GNSS receiver 51 identifies a position of the host vehicle M basedon a signal received from a GNSS satellite. The position of the hostvehicle M may be identified or corrected by an inertial navigationsystem (INS) using an output of the vehicle sensor 40. The navigationHMI 52 includes a display device, a speaker, a touch panel, keys, andthe like. The navigation HMI 52 may be partly or wholly shared with theabove-described HMI 30. For example, the route determiner 53 determinesa route (hereinafter referred to as a route on a map) from the positionof the host vehicle M identified by the GNSS receiver 51 (or any inputposition) to a destination input by the occupant using the navigationHMI 52 with reference to the first map information 54. The first mapinformation 54 is, for example, information in which a route shape (aroad shape) is expressed by a link indicating a road and nodes connectedby the link. The first map information 54 may include a curvature of aroad, point of interest (POI) information, and the like. The route onthe map is output to the MPU 60. The navigation device 50 may performroute guidance using the navigation HMI 52 based on the route on themap. The navigation device 50 may be implemented, for example, accordingto a function of a terminal device such as a smartphone or a tabletterminal possessed by the occupant. The navigation device 50 maytransmit a current position and a destination to a navigation server viathe communication device 20 and acquire a route equivalent to the routeon the map from the navigation server.

For example, the MPU 60 includes a recommended lane determiner 61 andstores second map information 62 in a storage device such as an HDD or aflash memory. The recommended lane determiner 61 divides the route onthe map provided from the navigation device 50 into a plurality ofblocks (for example, divides the route every 100 [m] in a travelingdirection of the vehicle), and determines a recommended lane for eachblock with reference to the second map information 62. The recommendedlane determiner 61 determines in what lane numbered from the left thevehicle will travel. The recommended lane determiner 61 determines therecommended lane so that the host vehicle M can travel along areasonable route for traveling to a branching destination when there isa branch point in the route on the map.

The second map information 62 is map information which has higheraccuracy than the first map information 54. For example, the second mapinformation 62 includes information about a center of a lane,information about a boundary of a lane, and the like. The second mapinformation 62 may include road information, traffic regulationsinformation, address information (an address/postal code), facilityinformation, telephone number information, and the like. The second mapinformation 62 may be updated at any time when the communication device20 communicates with another device.

For example, the driving operators 80 include an accelerator pedal, abrake pedal, a shift lever, a steering wheel, a steering wheel variant,a joystick, and other operators. A sensor configured to detect an amountof operation or the presence or absence of an operation is attached tothe driving operator 80, and a detection result thereof is output to theautomated driving control device 100 or some or all of the traveldriving force output device 200, the brake device 210, and the steeringdevice 220.

The automated driving control device 100 includes, for example, a firstcontroller 120 and a second controller 180. Each of the first controller120 and the second controller 180 is implemented, for example, by ahardware processor such as a central processing unit (CPU) executing aprogram (software). Some or all of these components are implemented byhardware (a circuit including circuitry) such as a large-scaleintegration (LSI) circuit, an application specific integrated circuit(ASIC), a field-programmable gate array (FPGA), or a graphics processingunit (GPU) or may be implemented by software and hardware incooperation. The program may be pre-stored in a storage device (astorage device including a non-transitory storage medium) such as an HDDor a flash memory of the automated driving control device 100 or may bestored in a removable storage medium such as a DVD or a CD-ROM andinstalled in the HDD or the flash memory of the automated drivingcontrol device 100 when the storage medium (the non-transitory storagemedium) is mounted in a drive device. The automated driving controldevice 100 is an example of a “mobile object control device.” At leastthe first controller 120 is an example of a “control system.” The“control system” may include the second controller 180.

FIG. 2 is a functional configuration diagram of the automated drivingcontrol device 100. The first controller 120 includes, for example, arecognizer 130, a risk distribution predictor 135, an action plangenerator 140, and an abnormality determiner 150. A combination of therisk distribution predictor 135, the action plan generator 140, and thesecond controller 180 is an example of a “movement controller.” Theabnormality determiner 150 is an example of a “determiner.”

The recognizer 130 recognizes states of a position, a speed,acceleration, and the like of a physical object around the host vehicleM based on information input from the camera 10, the radar device 12,and the LIDAR sensor 14 via the physical object recognition device 16.For example, the position of the physical object is recognized as aposition on absolute coordinates with a representative point (a centerof gravity, a driving shaft center, or the like) of the host vehicle Mas the origin and is used for control. The position of the physicalobject may be represented by a representative point such as a center ofgravity or a corner of the physical object or may be represented by arepresented region. The “state” of a physical object may includeacceleration or jerk of the physical object or an “action state” (forexample, whether or not a lane change is being made or intended).

For example, the recognizer 130 recognizes a lane in which the hostvehicle M is traveling (a travel lane). For example, the recognizer 130recognizes the travel lane by comparing a pattern of a road dividingline (for example, an arrangement of solid lines and broken lines)obtained from the second map information 62 with a pattern of roaddividing lines in the vicinity of the host vehicle M recognized from animage captured by the camera 10. The recognizer 130 may recognize atravel lane by recognizing a traveling path boundary including a roaddividing line, a road shoulder, a curb, a median strip, a guardrail, orthe like as well as a road dividing line. In this recognition, aposition of the host vehicle M acquired from the navigation device 50 ora processing result of the INS may be added. The recognizer 130recognizes a temporary stop line, an obstacle, red traffic light, a tollgate, and other road events.

When the travel lane is recognized, the recognizer 130 recognizes aposition or orientation of the host vehicle M with respect to the travellane. For example, the recognizer 130 may recognize a gap of a referencepoint of the host vehicle M from the center of the lane and an angleformed with respect to a line connecting the center of the lane in thetraveling direction of the host vehicle M as a relative position andorientation of the host vehicle M related to the travel lane.Alternatively, the recognizer 130 may recognize a position of thereference point of the host vehicle M related to one side end portion (aroad dividing line or a road boundary) of the travel lane or the like asa relative position of the host vehicle M related to the travel lane.

The risk distribution predictor 135 sets a risk which is an index valueindicating a degree at which the host vehicle M should not enter orapproach in an assumed plane S represented in a two-dimensional planewhen a space near the host vehicle M is viewed from above. In otherwords, the risk represents a probability of the presence of a target(including a road shoulder, a guardrail, a non-travelable area such asan area outside a white line as well as a physical object) (the risk maynot be a “probability” in the strict sense). An increase in a risk valuerepresents that the host vehicle M should not enter or approach and arisk value close to zero represents that it is preferable that the hostvehicle M travel. However, the above relationship may be reversed.

The risk distribution predictor 135 sets risks in the assumed plane Swith respect to a current time point and future time points defined atgiven time intervals such as a current time t, a time after Δt (timet+Δt), and a time after 2Δt (time t+2Δt). The risk distributionpredictor 135 predicts the risks at the future time points based on achange in the position of the target that is continuously recognized bythe recognizer 130.

FIG. 3 is a diagram showing an outline of the risk set by the riskdistribution predictor 135. The risk distribution predictor 135 sets afirst risk using an ellipse or a circle based on a traveling directionand a speed as a contour line on the assumed plane S with respect to atraffic participant (a physical object) such as a vehicle, a pedestrian,or a bicycle. The risk distribution predictor 135 sets a referencetarget trajectory based on the route shape recognized by the recognizer130. For example, the risk distribution predictor 135 sets the referencetarget trajectory at the center of a lane for a straight road and setsan arc-shaped reference target trajectory near the center of the lanefor a curved road. The risk distribution predictor 135 sets a secondrisk whose value is smallest at a position of the reference targettrajectory, increases as a distance from the reference target trajectoryto the non-travelable area increases, and becomes constant when thenon-travelable area is reached. In FIG. 3, DM denotes the travelingdirection of the host vehicle M and Kr denotes the reference targettrajectory. R1(M1) is the first risk of a stopped vehicle M1 and R1(P)is the first risk of a pedestrian P. Because the pedestrian P is movingin a direction in which he or she crosses a road, the first risk is setat a position different from that of the current time at each time pointin the future. The same is true for moving vehicles, bicycles, and thelike. R2 denotes a second risk. As shown in FIG. 3, a density ofhatching indicates the risk value and the risk is higher when thehatching is darker. FIG. 4 is a diagram showing values of the first riskR1 and the second risk R2 taken along line 4-4 of FIG. 3.

The action plan generator 140 includes a target trajectory generator145. In principle, the target trajectory generator 145 generates afuture target trajectory along which the host vehicle M is allowed totravel autonomously (independently of a driver's operation) so that thehost vehicle M travels in the recommended lane determined by therecommended lane determiner 61 and passes through a point where a riskset by the risk distribution predictor 135 (a sum of the first risk R1and the second risk R2) is small. For example, the target trajectoryincludes a speed element. For example, the target trajectory isrepresented by arranging a plurality of points (trajectory points) atwhich the host vehicle M is required to arrive in order from a pointclosest to the host vehicle M. The trajectory point is a point where thehost vehicle M is required to arrive for each predetermined travelingdistance (for example, about several meters [m]). On the other hand, atarget speed and target acceleration for each predetermined samplingtime period (for example, about several tenths of a second [sec]) aregenerated as parts of the target trajectory. The trajectory point may bea position at which the host vehicle M is required to arrive at thesampling time for each predetermined sampling time period. In this case,information of the target speed or the target acceleration isrepresented by an interval between trajectory points. The action plangenerator 140 generates a plurality of target trajectory candidates,calculates scores based on the viewpoints of efficiency and safety, andselects a target trajectory candidate having a high score as the targettrajectory. In the following description, a target trajectory, which isa set of trajectory points, may be illustrated in the form of a simplestraight line or a broken line.

The target trajectory generator 145 iteratively generates a targettrajectory based on a position and an orientation of the host vehicle Mand the reference target trajectory. FIG. 5 is a first diagram fordescribing a process of the target trajectory generator 145. In theexample of FIG. 5, because there is no object that causes the first riskR1, the target trajectory generator 145 generates the target trajectoryexclusively in consideration of the second risk R2. In FIG. 5, K denotesthe target trajectory and Kp denotes a trajectory point. In this state,the host vehicle M is offset to the left of the center of the travellane and is inclined to the right in an extending direction of thetravel lane. The target trajectory generator 145 generates the targettrajectory K so that the host vehicle M approaches a point on thereference target trajectory Kr with a low second risk R2 and avoidssudden turning or acceleration/deceleration. As a result, the targettrajectory K converges to the reference target trajectory Kr whiledrawing a smooth curve. As described above, the reference targettrajectory Kr serves as a reference when the target trajectory K isgenerated.

When there is an object that causes the first risk R1, the targettrajectory K is different from the form shown in FIG. 5. FIG. 6 is asecond diagram for describing the process of the target trajectorygenerator 145. In the example of FIG. 6, the first risk R1 affects theform of the target trajectory K. That is, the target trajectory K thattakes a detour to the right in order to avoid the vicinity of a stoppedvehicle M1 is generated. It is assumed that the first risk R1(P) causedby the pedestrian P does not affect the target trajectory K because thepedestrian P approaches the travel lane after the host vehicle M passesthrough the travel lane.

A function of the abnormality determiner 150 will be described below.

The second controller 180 controls the travel driving force outputdevice 200, the brake device 210, and the steering device 220 based onthe target trajectory generated by the first controller 120. Wheninformation of an amount of operation exceeding the reference has beeninput from the driving operator 80, the second controller 180 stops theautomated driving by the first controller 120 and switches the drivingto the manual driving.

The travel driving force output device 200 outputs a travel drivingforce (torque) for enabling the vehicle to travel to driving wheels. Forexample, the travel driving force output device 200 may include acombination of an internal combustion engine, an electric motor, atransmission, and the like, and an electronic control unit (ECU) thatcontrols the internal combustion engine, the electric motor, thetransmission, and the like. The ECU controls the above-describedcomponents in accordance with information input from the secondcontroller 180 or information input from the driving operator 80.

For example, the brake device 210 includes a brake caliper, a cylinderconfigured to transfer hydraulic pressure to the brake caliper, anelectric motor configured to generate hydraulic pressure in thecylinder, and a brake ECU. The brake ECU controls the electric motor inaccordance with the information input from the first controller 120 orthe information input from the driving operator 80 so that brake torqueaccording to a braking operation is output to each wheel. The brakedevice 210 may include a mechanism configured to transfer the hydraulicpressure generated by an operation of the brake pedal included in thedriving operators 80 to the cylinder via a master cylinder as a backup.The brake device 210 is not limited to the above-described configurationand may be an electronically controlled hydraulic brake deviceconfigured to control the actuator in accordance with information inputfrom the second controller 180 and transfer the hydraulic pressure ofthe master cylinder to the cylinder.

For example, the steering device 220 includes a steering ECU and anelectric motor. For example, the electric motor changes a direction ofsteerable wheels by applying a force to a rack and pinion mechanism. Thesteering ECU drives the electric motor in accordance with theinformation input from the second controller 180 or the informationinput from the driving operator 80 to cause the direction of thesteerable wheels to be changed.

[Abnormality Determination]

Hereinafter, content of a process of the abnormality determiner 150 willbe described. The abnormality determiner 150 determines whether or notan abnormality has occurred in the control system according to theprocess to be described below. The abnormality determiner 150 determinesthat an abnormality has occurred in the control system when a degree ofdeviation between a reference target trajectory determined by the routeshape recognized by the recognizer 130 and serving as a reference forgenerating the target trajectory in the target trajectory generator 145and a target trajectory is greater than or equal to a first referencedegree. When it is determined that an abnormality has occurred in thecontrol system, the abnormality determiner 150 causes the HMI 30 tooutput information for prompting the driver to perform maintenance andinspection of the host vehicle M.

FIG. 7 is a diagram for describing content of a process of obtaining adegree of deviation between the reference target trajectory and thetarget trajectory. For example, the abnormality determiner 150 derives nlateral position deviations ΔY_k, each of which is a distance between apoint where the reference target trajectory Kr intersects a virtual lineVL and a point where the target trajectory K intersects the virtual lineVL on the virtual line VL that divides the assumed plane S in thetraveling direction of the host vehicle M for each predetermineddistance in a certain monitoring section (k=1 to n). The lateralposition deviation ΔY_k indicates a deviation (a distance) between thecoordinate points corresponding to the same point in the travelingdirection of the host vehicle M on the reference target trajectory Krand the target trajectory K. Because the reference target trajectory Krand the target trajectory K may be represented as a collection ofpoints, the abnormality determiner 150 obtains points that intersect thevirtual line VL by performing linear interpolation or the like asnecessary. The monitoring section may be selected in accordance with anyrule.

The abnormality determiner 150 calculates Score1 indicating the degreeof deviation based on, for example, Eq. (1).

Score1=w1×(ΔY_1)² +w2×(ΔY_2)² + . . . +wn×(ΔY_n)²=Σ_(k=1) ^(n){wk×(ΔY_k)²}  (1)

In Eq. (1), wk is a weight coefficient. wk has a larger value when adegree of change obtained by comparing the lateral position deviationΔY_k with adjacent lateral position deviations ΔY_k−1 and ΔY_k+1 in atleast the traveling direction of the host vehicle M is higher. Forexample, the abnormality determiner 150 calculates a “degree of change”associated with the lateral position deviation ΔY_k by executing a fastFourier transform (FFT) on lateral position deviations ΔYk−5, ΔYk−4,ΔYk−3, ΔYk−2, ΔYk−1, ΔYk, ΔYk+1, ΔYk+2, ΔYk+3, ΔYk+4, and ΔYk+5including five points before and after the lateral position deviationΔY_k. That is, wk is represented by wk=f{FFT(k)}. f{ } is a function ofreturning a larger value when a frequency which is an FFT result ishigher (i.e., when a degree of change in the adjacent lateral positiondeviation is higher).

The abnormality determiner 150 determines whether or not Score1 ishigher than or equal to the first threshold value Th1 (an example of thefirst reference degree) and determines that an abnormality has occurredin the control system when Score1 is higher than or equal to the firstthreshold value Th1. The first threshold value Th1 is a value obtainedin advance by an experiment or the like so that the first thresholdvalue Th1 becomes a value near an upper limit of Score1 occurring in thecontrol system known to be operating normally. Alternatively, theabnormality determiner 150 may determine that an abnormality hasoccurred in the control system when the number of times that or theratio at which Score1 is greater than or equal to the first thresholdvalue Th1 is greater than or equal to the reference value as a result ofcalculating Score1 a predetermined number of times.

[Relaxation/stop condition of abnormality determination and others] WhenScore1 is intended to be calculated, the abnormality determiner 150 mayobtain a degree of risk obtained by aggregating values of first risks R1at trajectory points of the target trajectory K generated in themonitoring section and may not determine whether or not an abnormalityhas occurred in the control system with respect to the target sectionwhen the degree of risk is greater than or equal to a second thresholdvalue Th2 (an example of the second reference degree). A high degree ofrisk means that the presence of a physical object has a large effect onthe target trajectory. As a result, there is a high possibility that thereference target trajectory Kr and the actual trajectory L will deviatefrom each other as a normal phenomenon.

The abnormality determiner 150 may acquire surrounding environmentinformation of the host vehicle M and and make a probability of thedetermining that the abnormality has occurred lower when theenvironmental information satisfies a predetermined condition. Theenvironmental information is a time period, weather, a road surfacecondition, and the like and the predetermined condition is a conditionin which the performance of surroundings recognition by the recognizer130 and the accuracy of control of each device by the second controller180 are lowered. “Making a probability of the determining that theabnormality has occurred lower” means, for example, changing the firstthreshold value Th1 to a larger value or stopping determining whether ornot an abnormality has occurred in the control system. For example, thepredetermined condition is the “night time (for example, from 20:00 to5:00) and the rain of OO [mm] or more.”

The abnormality determiner 150 may acquire the speed of the host vehicleM from the vehicle speed sensor and make a probability of thedetermining that the abnormality has occurred lower when the speed ishigher than the reference speed. The meaning of “making a probability ofthe determining that the abnormality has occurred lower” is same asabove description.

The abnormality determiner 150 may collect a data set of the referencetarget trajectory and the actual trajectory in accordance with a speedrange of the host vehicle M (for example, defined in three stages of alow speed, a medium speed, and a high speed) and determine whether ornot an abnormality has occurred in the control system for each speedrange. In this case, the abnormality determiner 150 calculates Score1 apredetermined number of times for each speed range and determines thatan abnormality has occurred in the control system (in relation to thespeed range) when the number of times that or the ratio at which Score1is greater than or equal to the first threshold value Th1 is greaterthan or equal to the reference value. The abnormality determiner 150 maydetermine that an abnormality has occurred in the control system when anabnormality has occurred in the control system in relation to one speedrange or determine that an abnormality has occurred in the controlsystem when an abnormality has occurred in the control system inrelation to two or more speed ranges.

According to the above-described first embodiment, it is possible tofind some malfunction or performance deterioration and the like even ifthere is no apparent failure because it is determined that anabnormality has occurred in the control system when the degree ofdeviation between the reference target trajectory Kr based on the routeshape recognized by the recognizer 130 and the target trajectory Kgenerated by the target trajectory generator 145 is greater than orequal to the first threshold value Th1. That is, the abnormalitydetermination of the control system can be performed promptly.Conventionally, failure diagnosis on each part constituting the vehiclesystem has been put into practical use, but for example, whether thecombination of hardware and software components is correct for theentire control system related to autonomous driving is not sufficientlyverified. On the other hand, in the first embodiment, it is possible todetect that the entire control system is operating normally because theabnormality is determined based on an event that should converge if aninfluence of disturbance such as a physical object near the host vehicleM is small.

Second Embodiment

Hereinafter, a second embodiment will be described. The abnormalitydeterminer 150 of the first embodiment determines that an abnormalityhas occurred in the control system when the degree of deviation betweenthe reference target trajectory determined by the route shape recognizedby the recognizer 130 and serving as the reference for generating thetarget trajectory and the target trajectory is greater than or equal tothe first reference degree. On the other hand, an abnormality determiner150 of the second embodiment determines that an abnormality has occurredin a control system when a degree of deviation between a first targettrajectory generated at a first time point and a second targettrajectory generated at a second time point different from the firsttime point is greater than or equal to a third reference degree while atarget trajectory generator 145 iteratively generates the targettrajectory. It is only necessary for a relationship between the firsttime point and the second time point to be a relationship in which thefirst target trajectory and the second target trajectory overlap atleast partly in a traveling direction of a host vehicle M.

FIG. 8 is a diagram for describing content of a process of obtaining adegree of deviation between the first target trajectory and the secondtarget trajectory. For example, the abnormality determiner 150 derives mlateral position deviations ΔY_k, each of which is a distance between apoint where a first target trajectory K1 intersects a virtual line VLand a point where a second target trajectory K2 intersects the virtualline VL on the virtual line VL that divides the assumed plane S in thetraveling direction of the host vehicle M for each predetermineddistance in a certain monitoring section (k=1 to m). The lateralposition deviation ΔY_k indicates a deviation (a distance) between thecoordinate points corresponding to the same point in the travelingdirection of the host vehicle M on the first target trajectory K1 andthe second target trajectory K2. Because the first target trajectory K1and the second target trajectory K2 may be represented as a collectionof points, the abnormality determiner 150 obtains a point intersectingthe virtual line VL by performing linear interpolation and the like asnecessary. The monitoring section may be selected in accordance with anyrule.

The abnormality determiner 150 calculates Score2 indicating the degreeof dissociation based on, for example, Eq. (2).

Score2=w1×(ΔY_1)² +w2×(ΔY_2)² + . . . +wn×(ΔY_n)²=Σ_(k=1) ^(m){wk×(ΔY_k)²}  (2)

In Eq. (2), wk is a weight coefficient similar to that of the firstembodiment. The abnormality determiner 150 determines whether or notScore2 is greater than or equal to a third threshold value Th3 (anexample of the third reference degree) and determines that anabnormality has occurred in the control system when Score2 is greaterthan or equal to the third threshold value Th3. The third thresholdvalue Th3 is a value obtained in advance by an experiment or the like sothat third threshold value Th3 becomes a value near an upper limit ofScore2 occurring in the control system known to be operating normally.Alternatively, the abnormality determiner 150 may determine that anabnormality has occurred in the control system when the number of timesthat or the ratio at which Score2 is greater than or equal to the thirdthreshold value Th3 is greater than or equal to the reference value as aresult of calculating Score2 a predetermined number of times.

In relation to [Relaxation/stop condition of abnormality determinationand others], the second embodiment is similar to the first embodiment.

According to the second embodiment described above, effects similar tothose of the first embodiment can be obtained.

[Hardware Configuration]

FIG. 9 is a diagram showing an example of a hardware configuration ofthe automated driving control device 100 according to the embodiment. Asshown in FIG. 9, the automated driving control device 100 has aconfiguration in which a communication controller 100-1, a CPU 100-2, arandom access memory (RAM) 100-3 used as a working memory, a read onlymemory (ROM) 100-4 storing a boot program and the like, a storage device100-5 such as a flash memory or a hard disk drive (HDD), a drive device100-6, and the like are mutually connected by an internal bus or adedicated communication line. The communication controller 100-1communicates with components other than the automated driving controldevice 100. The storage device 100-5 stores a program 100-5 a to beexecuted by the CPU 100-2. This program is loaded into the RAM 100-3 bya direct memory access (DMA) controller (not shown) or the like andexecuted by the CPU 100-2. Thereby, some or all of the first controller120 and the second controller 180 are implemented.

Although modes for carrying out the present invention have beendescribed using embodiments, the present invention is not limited to theembodiments and various modifications and substitutions can also be madewithout departing from the scope and spirit of the present invention.

What is claimed is:
 1. A mobile object control method comprising:recognizing physical objects near a mobile object and a route shape;generating a target trajectory based on a result of the recognition;causing the mobile object to travel autonomously along the targettrajectory; and determining that an abnormality has occurred in acontrol system for causing the mobile object to travel autonomously byperforming the recognition when a degree of deviation between areference target trajectory determined by the route shape and serving asa reference for generating the target trajectory and the targettrajectory is greater than or equal to a first reference degree andoutput a determination result.
 2. The mobile object control methodaccording to claim 1, further comprising: performing a process ofassigning a larger weight when a degree of change obtained by comparinga deviation between coordinate points corresponding to the same point ina traveling direction of the mobile object on the reference targettrajectory and the target trajectory with a deviation between individualdata elements corresponding to adjacent points in at least the travelingdirection of the mobile object is higher in correspondence with the samepoint in a traveling direction of a plurality of mobile objects; andcalculating a degree of deviation between the reference targettrajectory and the target trajectory by aggregating deviations betweenthe individual data elements that have been weighted.
 3. A mobile objectcontrol method comprising: recognizing physical objects near a mobileobject and a route shape; iteratively generating a target trajectorybased on a result of the recognition; causing the mobile object totravel autonomously along the iteratively generated target trajectory;and determining that an abnormality has occurred in a control system forcausing the mobile object to travel autonomously by performing therecognition when a degree of deviation between a first target trajectorygenerated at a first time point and a second target trajectory generatedat a second time point different from the first time point is greaterthan or equal to a third reference degree and output a determinationresult.
 4. The mobile object control method according to claim 3,further comprising: performing a process of assigning a larger weightwhen a degree of change obtained by comparing a deviation betweencoordinate points corresponding to the same point in a travelingdirection of the mobile object on the first target trajectory and thesecond target trajectory with a deviation between coordinate pointscorresponding to adjacent points in at least the traveling direction ofthe mobile object is higher in correspondence with the same point in atraveling direction of a plurality of mobile objects; and, calculating adegree of deviation between the first target trajectory and the secondtarget trajectory by aggregating deviations between the coordinatepoints that have been weighted.
 5. The mobile object control methodaccording to claim 1, wherein the generating includes setting a riskthat is an index value representing a degree at which the mobile objectshould not approach based on the presence of the recognized physicalobject in an assumed plane represented in a two-dimensional plane when aspace near the mobile object is viewed from above and generates thetarget trajectory so that the mobile object passes through a point wherethe risk is low, and wherein the method further comprises stopping thedetermining when a degree of risk based on a risk value due to thepresence of the physical object at each point of the target trajectoryis higher than or equal to a second reference degree.
 6. The mobileobject control method according to claim 1, further comprising:acquiring surrounding environment information of the mobile object; andmaking a probability of the determining lower when the environmentinformation satisfies a predetermined condition.
 7. The mobile objectcontrol method according to claim 1, further comprising: acquiring aspeed of the mobile object; and making a probability of the determininglower when the speed is higher than a reference speed.
 8. The mobileobject control method according to claim 1, further comprising:collecting data for each speed range of the mobile object; and whereinthe determining is performed for each speed range of the mobile object.9. A mobile object control device comprising: a storage device storing aprogram; and a hardware processor, wherein the hardware processorexecutes the program stored in the storage device to recognize physicalobjects near a mobile object and a route shape; generate a targettrajectory based on a result of the recognition; cause the mobile objectto travel autonomously along the target trajectory; and determine thatan abnormality has occurred in a control system for causing the mobileobject to travel autonomously by performing the recognition when adegree of deviation between a reference target trajectory determined bythe route shape and serving as a reference for generating the targettrajectory and the target trajectory is greater than or equal to a firstreference degree and output a determination result.
 10. A mobile objectcontrol device comprising: a storage device storing a program; and ahardware processor, wherein the hardware processor executes the programstored in the storage device to recognize physical objects near a mobileobject and a route shape; iteratively generate a target trajectory basedon a result of the recognition; cause the mobile object to travelautonomously along the iteratively generated target trajectory; anddetermine that an abnormality has occurred in a control system forcausing the mobile object to travel autonomously by performing therecognition when a degree of deviation between a first target trajectorygenerated at a first time point and a second target trajectory generatedat a second time point different from the first time point is greaterthan or equal to a third reference degree and output a determinationresult.
 11. A computer-readable non-transitory storage medium storing aprogram to be executed by a computer to: recognize physical objects neara mobile object and a route shape; generate a target trajectory based ona result of the recognition; cause the mobile object to travelautonomously along the target trajectory; and determine that anabnormality has occurred in a control system for causing the mobileobject to travel autonomously by performing the recognition when adegree of deviation between a reference target trajectory determined bythe route shape and serving as a reference for generating the targettrajectory and the target trajectory is greater than or equal to a firstreference degree and output a determination result.
 12. Acomputer-readable non-transitory storage medium storing a program to beexecuted by a computer to: recognize physical objects near a mobileobject and a route shape; iteratively generate a target trajectory basedon a result of the recognition; cause the mobile object to travelautonomously along the iteratively generated target trajectory; anddetermine that an abnormality has occurred in a control system forcausing the mobile object to travel autonomously by performing therecognition when a degree of deviation between a first target trajectorygenerated at a first time point and a second target trajectory generatedat a second time point different from the first time point is greaterthan or equal to a third reference degree and output a determinationresult.